System and method for evaluating camouflage pattern based on image analysis

ABSTRACT

According to an embodiment, a system comprises a communication module providing a communication interface, a camouflage pattern evaluation module performing an artificial intelligence-based camouflage pattern evaluation algorithm on an operation environment image and a camouflage pattern image, analyzing a similarity between the operation environment image and the camouflage pattern image, and obtaining an evaluation result of camouflage performance for the camouflage pattern in the operation environment, and a processor deriving a quantitative camouflage performance value for the evaluation result. The artificial intelligence-based camouflage performance evaluation algorithm extracts feature information for the operation environment image and the camouflage pattern image and analyzes the similarity in color, pattern, or structure between the operation environment image and the camouflage pattern image based on the extracted feature information.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. 119 toKorean Patent Application No. 10-2020-0157570, filed on Nov. 23, 2020,in the Korean Intellectual Property Office, the disclosure of which isherein incorporated by reference in its entirety.

TECHNICAL FIELD

The disclosure relates to a camouflage pattern evaluation technique forevaluating the camouflage performance of a camouflage pattern.

DESCRIPTION OF RELATED ART

The description of the Discussion of Related Art section merely providesinformation that may be relevant to embodiments of the disclosure butshould not be appreciated as necessarily constituting the prior art.

Military weapons may be camouflaged. Military camouflage is the use ofcamouflage by a military force to protect personnel and equipment fromobservation by enemy forces Realizing the necessity of such camouflage,the military has prepared a number of concealment functions formaterials, equipment and facilities used in the military.

In particular, to conceal military structures, military facilities, orground weapon systems, the military has conventionally camouflagepatterns with paint. However, the painted patterns may be easilyscratched or erased by an external physical impact and discolored orpeeled off over time.

The camouflage pattern is used to maximize the camouflage function ofsoldiers and weapon systems by simulating the colors and patterns of thesurrounding environment during operation and thus minimize damage tolives and facilities.

Recently, as the military operational environment and missions arecomplicated and diversified, a need arises for research and developmentto simply create camouflage patterns optimized for the battlefieldenvironment and to evaluate the camouflage performance of pre-createdcamouflage patterns.

Camouflage patterns are used to reduce detection probability and toobstruct aiming. However, the conventional camouflage technology usingpreviously defined camouflage patterns focuses only on aimingobstruction. The conventional art relies heavily on the high contrasteffect of colors with large differences in brightness, saturation, andhue and relatively simple patterns. Therefore, conventional combatuniforms or equipment having conventional camouflage patterns sufferfrom reduced camouflage effect due to the absence of a quantitativeanalysis of the recent operational environment.

SUMMARY

According to an embodiment, to address the foregoing issues, an objectof the disclosure is to receive an operation environment image and acamouflage pattern image and evaluate whether the camouflage performanceof the camouflage pattern in the operation environment is appropriateusing artificial intelligence-based image analysis.

However, the objects of the embodiments are not limited thereto, andother objects may also be present.

According to an embodiment, a system for evaluating a camouflage patternbased on image analysis comprises a communication module providing acommunication interface interworking with a communication module toprovide a transmission/reception signal in a packet data form, acamouflage pattern evaluation module performing an artificialintelligence-based camouflage pattern evaluation algorithm on anoperation environment image of an operation environment and a camouflagepattern image of a camouflage pattern received through the communicationmodule, analyzing a similarity between the operation environment imageand the camouflage pattern image, and obtaining an evaluation result ofcamouflage performance for the camouflage pattern in the operationenvironment, and a processor deriving and providing a quantitativecamouflage performance value for the evaluation result of the camouflageperformance obtained by the camouflage pattern evaluation module. Theartificial intelligence-based camouflage performance evaluationalgorithm extracts feature information for the operation environmentimage and the camouflage pattern image and analyzes the similarity incolor, pattern, or structure between the operation environment image andthe camouflage pattern image based on the extracted feature information.

According to an embodiment, the camouflage pattern evaluation module mayinclude an image input unit receiving the operation environment imageand the camouflage pattern image, an image preprocessor preprocessingthe operation environment image and the camouflage pattern image throughcorrection and normalization, a similarity analyzer analyzingsimilarities in color, pattern, and structure between the operationenvironment image and the camouflage pattern image preprocessed by theimage preprocessor, and a result providing unit averaging thesimilarities in color, pattern, and structure between the operationenvironment image and the camouflage pattern image, calculating aweighted sum, and providing the evaluation result of the camouflageperformance.

According to an embodiment, the similarity analyzer may include a colorsimilarity analyzer calculating a color similarity by performing colorcomparison on each pixel between the operation environment image and thecamouflage pattern image, a pattern similarity analyzer calculating apattern similarity by performing comparison on a color distribution ineach space between the operation environment image and the camouflagepattern image, and a structural similarity analyzer extractingrespective structural feature vectors of the operation environment imageand the camouflage pattern image using a deep learning-based objectrecognition algorithm and calculating a structural similarity betweenthe extracted structural feature vectors. According to an embodiment,the camouflage pattern evaluation module scans the entire operationenvironment image using a sliding window scheme and calculates thesimilarity between the operation environment image and the camouflagepattern image for each sliding window.

According to an embodiment, the camouflage pattern evaluation modulerepeats the calculation of the similarity between the operationenvironment image and the camouflage pattern image for each slidingwindow while resizing the sliding window by a preset increment ordecrement.

According to an embodiment, a method for evaluating a camouflage patternbased on image analysis performed by an image analysis system comprisesreceiving an operation environment image of an operation environment anda camouflage pattern image of a camouflage pattern, performingpreprocessing on the operation environment image and the camouflagepattern image through correction and normalization, analyzing asimilarity between the operation environment image and the camouflagepattern image using an artificial intelligence-based camouflageperformance evaluation algorithm, and deriving and providing aquantitative camouflage performance value for an evaluation result of acamouflage performance for the camouflage pattern in the operationenvironment. The artificial intelligence-based camouflage performanceevaluation algorithm extracts feature information for the operationenvironment image and the camouflage pattern image and analyzes thesimilarity in color, pattern, or structure between the operationenvironment image and the camouflage pattern image based on theextracted feature information.

According to an embodiment, performing the preprocessing may includeconverting the operation environment image and the camouflage patternimage, represented as red-green-blue (RGB) color space data, into XYZcolor space data, and converting the XYZ color space data into Lab colorspace data.

According to an embodiment, analyzing the similarity may include a colorsimilarity analysis step calculating a color similarity by performingcolor comparison on each pixel between the operation environment imageand the camouflage pattern image, a pattern similarity analysis stepcalculating a pattern similarity by performing comparison on a colordistribution in each space between the operation environment image andthe camouflage pattern image, and a structural similarity analysis stepextracting respective structural feature vectors of the operationenvironment image and the camouflage pattern image using a deeplearning-based object recognition algorithm and calculating a structuralsimilarity between the extracted structural feature vectors.

According to an embodiment, the color similarity analysis step mayinclude measuring the color similarity by converting the operationenvironment image and the camouflage pattern image, represented as RGBcolor space data, into Lab color space data and then calculating a colordifference based on a preset color difference equation between pixelsmatching between the operation environment image and the camouflagepattern image in the Lab color space data.

According to an embodiment, the pattern similarity analysis step mayinclude measuring a degree of reflection of a color distribution of thecamouflage pattern image as compared with the operation environmentimage using any one index of a structural similarity index measure(SSIM) or a multiple SSIM (MSSIM) for measuring a structural similarityindex.

According to an embodiment, the deep learning-based object recognitionalgorithm used in the structural similarity analysis step uses aconvolutional neural network (CNN) including a visual geometry groupnetwork (VGGNET) and a residual network (ResNET). The deeplearning-based object recognition algorithm extracts respective featuremaps of the operation environment image and the camouflage pattern imageand compares the extracted feature maps through a Frobenius norm tomeasure the structural similarity between the operation environmentimage and the camouflage pattern image.

According to an embodiment, deriving and providing the quantitativecamouflage performance value may include deriving and providing thequantitative camouflage performance value by multiplying each ofsimilarities in color, pattern, or structure of the evaluation result ofthe camouflage performance by a preset weight and summating theweight-multiplied similarities.

According to the embodiments of the disclosure, it is possible toquantitatively evaluate the camouflage performance of a specificcamouflage pattern in a specific operation environment using artificialintelligence-based image analysis and to simply generate a camouflagepattern optimized for the operation environment or to quickly evaluatethe camouflage performance of a previously created camouflage pattern.

Further, according to the embodiments of the disclosure, it is possibleto maximize the camouflage function of soldiers or facilities bysimulating the color and pattern of the ambient environment during(military) operation, thus minimizing damage to lives or facilities. Itis also possible to quickly and precisely evaluate the camouflageperformance of a camouflage pattern in diversified operationenvironments, thus leading to cost savings in determining or adopting acamouflage pattern.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the disclosure and many of the attendantaspects thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 is a block diagram illustrating a configuration of an imageanalysis-based camouflage pattern evaluation system according to anembodiment of the disclosure;

FIG. 2 is a block diagram illustrating a configuration of a camouflagepattern evaluation module according to an embodiment of the disclosure;

FIG. 3 is a view illustrating functions of a camouflage patternevaluation module according to an embodiment of the disclosure;

FIG. 4 is a flowchart illustrating an image analysis-based camouflagepattern evaluation method according to an embodiment of the disclosure;

FIG. 5 is a view illustrating a similarity analysis process according toan embodiment of the disclosure;

FIG. 6 is a view illustrating a CNN model applied to a deeplearning-based object recognition algorithm according to an embodimentof the disclosure; and

FIG. 7 is a view illustrating a process of extracting a feature mapthrough a deep learning-based object recognition algorithm according toan embodiment of the disclosure.

DETAILED DESCRIPTION

Hereinafter, exemplary embodiments of the inventive concept will bedescribed in detail with reference to the accompanying drawings. Theinventive concept, however, may be modified in various different ways,and should not be construed as limited to the embodiments set forthherein. Like reference denotations may be used to refer to the same orsimilar elements throughout the specification and the drawings. However,the disclosure may be implemented in other various forms and is notlimited to the embodiments set forth herein. For clarity of thedisclosure, irrelevant parts are removed from the drawings, and similarreference denotations are used to refer to similar elements throughoutthe specification.

In embodiments of the disclosure, when an element is “connected” withanother element, the element may be “directly connected” with the otherelement, or the element may be “electrically connected” with the otherelement via an intervening element. When an element “comprises” or“includes” another element, the element may further include, but ratherthan excluding, the other element, and the terms “comprise” and“include” should be appreciated as not excluding the possibility ofpresence or adding one or more features, numbers, steps, operations,elements, parts, or combinations thereof.

In the disclosure, the term ‘terminal’ or ‘terminal device’ may refer toa wireless communication device with portability and mobility, and maybe any kind of handheld wireless communication device, such as a smartphone, a tablet PC, or a laptop computer. The term ‘terminal’ or‘terminal device’ may refer to a wired communication device, such as apersonal computer (PC) that may access other terminals or servers usinga network. Here, the network means a connection structure capable ofexchanging information between nodes, such as a plurality of terminalsor servers, and examples of the network include local area networks(LANs), wide area networks (WANs), internet (world wide web (WWW)),wired/wireless data communication networks, telephony networks, orwired/wireless television communication networks.

Examples of wireless data communication networks may include, but arenot limited to, 3G, 4G, 5G, 3rd generation partnership project (3GPP),long term evolution (LTE), world interoperability for microwave access(WIMAX), Wi-Fi, Bluetooth communication, infrared communication,ultrasound communication, visible light communication (VLC), and Li-Fi.

Example embodiments are described below for a better understanding ofthe disclosure, but the disclosure is not limited thereto. Therefore, itshould be noted that any embodiment performing substantially the samefunction as the embodiments disclosed herein belong to the scope of thedisclosure.

The components, processes, steps, or methods according to embodiments ofthe disclosure may be shared as long as they do not technically conflictwith each other.

Hereinafter, embodiments of the disclosure are described in detail withreference to the accompanying drawings.

FIG. 1 is a block diagram illustrating a configuration of an imageanalysis-based camouflage pattern evaluation system according to anembodiment of the disclosure.

Referring to FIG. 1, an image analysis-based camouflage patternevaluation system may include, but is not limited to, a communicationmodule 210, a camouflage pattern evaluation module 220, a memory 240, aprocessor 230, and a database 250.

The communication module 210 provides a communication interferencenecessary to interwork with a communication network to transmit/receivesignals, e.g., packets, to/from an image providing device 100. Thecommunication module 210 may receive a data request from a user terminaland may transmit data in response to the data request. The communicationmodule 210 may be a device including hardware or software necessary totransmit/receive signals such as control signals or data signals viawired/wireless communication with other network devices.

Here, the user terminal may request to evaluate a camouflage pattern ormay input a camouflage pattern (or camouflage pattern image) or anoperation site image to be evaluated.

If the communication module 210 receives an operation environment image(or operation site image) and a camouflage pattern image, the camouflagepattern evaluation module 220 performs an artificial intelligence(AI)-based camouflage performance evaluation algorithm to analyze thesimilarity between the operation environment image and the camouflagepattern image, thereby evaluating the camouflage performance of thecamouflage pattern for the operation environment. The artificialintelligence-based camouflage performance evaluation algorithm extractsfeature information for the operation environment image and thecamouflage pattern image and analyzes the similarity in color, patternor structure between the operation environment image and the camouflagepattern image based on the extracted feature information.

The memory 240 stores a program for performing an image analysis-basedcamouflage pattern evaluation method including the artificialintelligence-based camouflage performance evaluation algorithm. Further,the memory 240 stores data processed by the processor 230 temporarily orpermanently. The memory 240 may include, but is not limited to, volatilestorage media or non-volatile storage media.

The processor 230 controls the entire process of providing the imageanalysis-based camouflage pattern evaluation method. The processor 230may derive a quantitative camouflage performance value, calculated, forthe result of evaluation of the camouflage performance and output thequantitative camouflage performance value on the screen or provide thequantitative camouflage performance value to the user terminal.

The processor 170 may include any kind or type of device capable ofprocessing data. As used herein, ‘processor’ may refer to a dataprocessing device embedded in hardware and having a physicallystructured circuit to perform functions represented in codes or commandsincluded in the program. Examples of the data processing device embeddedin hardware may include microprocessors, central processing units(CPUs), processor cores, multi-processors, application-specificintegrated circuits (ASICs), field programmable gate arrays (FPGAs), orother processing devices, but the scope of the disclosure is not limitedthereto.

The database 250 stores data accumulated while performing the imageanalysis-based camouflage pattern evaluation method. For example, thedatabase 250 may store, e.g., the operation environment image, thecamouflage pattern image, the evaluation result of camouflageperformance, or the quantitative camouflage performance value.

FIG. 2 is a block diagram illustrating a configuration of a camouflagepattern evaluation module according to an embodiment of the disclosure.FIG. 3 is a view illustrating functions of a camouflage patternevaluation module according to an embodiment of the disclosure.

Referring to FIGS. 2 and 3, the camouflage pattern evaluation module 220includes an image input unit 221, an image preprocessor 222, asimilarity analyzer 223, and a result providing unit 224.

The image input unit 221 receives an operation environment image and acamouflage pattern image. The operation environment image may be stillimage or video data having a format of, e.g., BMP, JPG, PNG, MP4, orAVI, captured (or recorded or obtained) during the day or night. Thecamouflage pattern image may be still image data having a format of,e.g., BMP, JPG, or PNG, captured or obtained for a camouflage pattern tobe evaluated (i.e., an evaluation target).

The image preprocessor 222 performs preprocessing, such as, e.g.,correction and normalization, on the operation environment image and thecamouflage pattern image. The image preprocessor 222 performs colorspace conversion to reduce color space errors.

The operation environment image and camouflage pattern image arerepresented in RGB color space. Accordingly, the image preprocessor 222converts the color representation of each pixel in the image into theLab color space, which is a color space based on a human colorrecognition process. The Lab color space is device-independent, unlikeRGB and CMYK color spaces where colors vary depending on displayequipment or print media. Therefore, the color difference equation inthe Lab color space is capable of color difference calculation with lessdistortion using CIEDE2000 while considering human color perception,thereby reducing color space errors.

The similarity analyzer 223 analyzes the similarity in color, pattern,and structure between the operation environment image and the camouflagepattern image preprocessed by the image preprocessor 222. The similarityanalyzer may include a color similarity analyzer 223 a, a patternsimilarity analyzer 223 b, and a structural similarity analyzer 223 c.

The color similarity analyzer 223 a calculates the color similarity byperforming color comparison on each pixel between the operationenvironment image and the camouflage pattern image. The patternsimilarity analyzer 223 b calculates the pattern similarity byperforming comparison on the color distribution in each space betweenthe operation environment image and the camouflage pattern image. Thestructural similarity analyzer 223 c may extract the respectivestructural feature vectors of the operation environment image and thecamouflage pattern image using a deep learning-based object recognitionalgorithm and compares the extracted structural feature vectors of theimages using the Frobenius norm to thereby calculate the structuralsimilarity between the two images.

The result providing unit 224 averages the similarities of color,pattern, and structure between the operation environment image and thecamouflage pattern image analyzed by the similarity analyzer 223 andthen calculates a weighted sum and provides the evaluation result of thecamouflage performance.

FIG. 4 is a flowchart illustrating an image analysis-based camouflagepattern evaluation method according to an embodiment of the disclosure.

Referring to FIG. 4, if an operation environment image and a camouflagepattern image are received (S10), the camouflage pattern evaluationmodule 220 performs preprocessing of color space conversion to reduce acolor space error (S20).

For example, the camouflage pattern evaluation module 220 converts theoperation environment image and the camouflage pattern image representedas RGB color space data into the following XYZ color space data usingEquation 1 below, and the camouflage pattern evaluation module 220converts the converted XYZ color space data into Lab color space datausing Equation 2 below.

$\begin{matrix}\left. \begin{bmatrix}X \\Y \\Z\end{bmatrix}\leftarrow{\begin{bmatrix}0.412453 & 0.357580 & 0.180423 \\0.212671 & 0.715160 & 0.072169 \\0.019334 & 0.119193 & 0.950227\end{bmatrix} \cdot \begin{bmatrix}R \\G \\B\end{bmatrix}} \right. & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack \\{{\left. X\leftarrow{X/X_{n}} \right.,{{{where}\mspace{14mu} X_{n}} = 0.950456}}{\left. Z\leftarrow{Z/Z_{n}} \right.,{{{where}\mspace{14mu} Z_{n}} = 1.088754}}\left. L\leftarrow\left\{ {\begin{matrix}{{116*Y^{1/3}} - 16} & {{{for}\mspace{14mu} Y} > 0.008856} \\{903.3*Y} & {{{for}\mspace{14mu} Y} \leq 0.008856}\end{matrix}a}\leftarrow{{500\left( {{f(X)} - {f(Y)}} \right)} + {{delta}b}}\leftarrow{{200\left( {{f(Y)} - {f(Z)}} \right)} + {delta}} \right. \right.} & \left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack\end{matrix}$

The camouflage pattern evaluation module 220 analyzes the similaritybetween the operation environment image and the camouflage pattern imageusing an artificial intelligence-based camouflage performance evaluationalgorithm. The camouflage pattern evaluation module 220 performs a colorsimilarity analysis step S30 of calculating the color similarity byperforming color comparison on each pixel between the operationenvironment image and the camouflage pattern image, a pattern similarityanalysis step S40 of calculating the pattern similarity by performingcomparison on the color distribution in each space between the operationenvironment image and the camouflage pattern image, and a structuralsimilarity analysis step S50 of extracting the respective structuralfeature vectors of the operation environment image and the camouflagepattern image using a deep learning-based object recognition algorithmand calculating the structural similarity for the extracted structuralfeature vectors between the images.

In this case, the camouflage pattern evaluation module 220 may scan theentire operation environment image using a sliding window technique andcalculates each of the color similarity, pattern similarity, andstructural similarity between the operation environment image and thecamouflage pattern image for each sliding window.

Further, the camouflage pattern evaluation module 220 repeats the stepor process of calculating the similarity between the operationenvironment image and the camouflage pattern image for each slidingwindow while changing the size of the sliding window according to apreset increment or decrement. The camouflage pattern evaluation module220 performs camouflage pattern performance evaluation calculationconsidering the camouflage pattern movement and perspective. Thecamouflage pattern evaluation module 220 calculates a weighted sum afteraveraging the color similarity, pattern similarity, and structuralsimilarity calculated for each sliding window, thereby deriving anevaluation result of camouflage performance.

Camouflage Effect(E,C)=w ₀ Clr(E,C)+w ₁ Ptn(E,C)+w ₂ Str(E,C)  [Equation3]

In Equation 3, E is the daytime operation environment image, C is thecamouflage pattern image, w_(i) is the weight, Clr(E, C) is the colorsimilarity, Ptn(E, C) is the pattern similarity, and Str(E, C) is thestructural similarity.

The processor 230 derives and provides a quantitative camouflageperformance value for the camouflage performance evaluation result ofthe camouflage pattern evaluation module 220 (S60).

Steps S10 to S60 of FIG. 4 may be divided into additional sub-steps ormay be combined into fewer steps according to embodiments of thedisclosure. Further, some of the steps may be omitted as necessary, orthe order of the steps may be changed.

FIG. 5 is a view illustrating a similarity analysis process according toan embodiment of the disclosure. FIG. 6 is a view illustrating a CNNmodel applied to a deep learning-based object recognition algorithmaccording to an embodiment of the disclosure. FIG. 7 is a viewillustrating a process of extracting a feature map through a deeplearning-based object recognition algorithm according to an embodimentof the disclosure.

Referring to FIGS. 5 to 7, the color similarity analyzer 223 a measuresthe color similarity by calculating the color difference based on apreset color difference equation (e.g., CIED2000) between pixelsmatching between the operation environment image and the camouflagepattern image in the Lab color space according to Equation 4 below.

Color difference equations (or formulas) between the two color spaces,i.e., (L₁*,a₁*,b₁*) and (L₂*,a₂*,b₂*), which are converted Lab colorspaces, may be Equations 4 to 13 below. ΔE is the difference inbrightness, saturation, and hue in the L*a*b*L coordinates and isdefined in the L*C*h*color space.

$\begin{matrix}{{\Delta E_{00}^{*}} = \sqrt{\left( \frac{\Delta\; L^{\prime}}{k_{L}S_{L}} \right)^{2} - \left( \frac{\Delta\; C^{\prime}}{k_{C}S_{C}} \right)^{2} - \left( \frac{\Delta\; H^{\prime}}{k_{H}S_{H}} \right)^{2} + {R_{T}\frac{\Delta\; C^{\prime}}{k_{C}S_{C}}\frac{\Delta\; H^{\prime}}{k_{H}S_{H}}}}} & \left\lbrack {{Equation}\mspace{14mu} 4} \right\rbrack \\{\mspace{79mu}{{\Delta\; L^{\prime}} = {L_{2}^{*} - L_{1}^{*}}}} & \left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack \\{\mspace{79mu}{C_{1}^{*} = \sqrt{a_{1}^{*2} + b_{1}^{*2}}}} & \left\lbrack {{Equation}\mspace{14mu} 6} \right\rbrack \\{\mspace{79mu}{C_{2}^{*} = \sqrt{a_{2}^{*2} + b_{2}^{*2}}}} & \left\lbrack {{Equation}\mspace{14mu} 7} \right\rbrack \\{\mspace{79mu}{{\overset{\_}{L}}^{\prime} = {{\frac{L_{1}^{*} + L_{2}^{*}}{2}\mspace{14mu}\overset{\_}{C}} = \frac{C_{1}^{*} + C_{2}^{*}}{2}}}} & \left\lbrack {{Equation}\mspace{14mu} 8} \right\rbrack \\{\mspace{79mu}{{a_{1}^{\prime} = {a_{1}^{*} + {\frac{a_{1}^{*}}{2}\left( {1 - \sqrt{\frac{{\overset{\_}{C}}^{7}}{{\overset{\_}{C}}^{7} + 25^{7}}}} \right)}}}\mspace{79mu}{a_{2}^{\prime} = {a_{2}^{*} + {\frac{a_{2}^{*}}{2}\left( {1 - \sqrt{\frac{{\overset{\_}{C}}^{7}}{{\overset{\_}{C}}^{7} + 25^{7}}}} \right)}}}}} & \left\lbrack {{Equation}\mspace{14mu} 9} \right\rbrack \\{\mspace{79mu}{{h_{1}^{\prime} = {a\;\tan\; 2\left( {b_{1}^{*},a_{1}^{\prime}} \right)\mspace{14mu}{mod}\mspace{14mu} 360{^\circ}}},\mspace{79mu}{h_{2}^{\prime} = {a\;\tan\; 2\left( {b_{2}^{*},a_{2}^{\prime}} \right)\mspace{14mu}{mod}\mspace{14mu} 360{^\circ}}}}} & \left\lbrack {{Equation}\mspace{14mu} 10} \right\rbrack \\{{\Delta\; h^{\prime}} = \left\{ \begin{matrix}{h_{2}^{\prime} - h_{1}^{\prime}} & {{{h_{1}^{\prime} - h_{2}^{\prime}}} \leq {180{^\circ}}} \\{h_{2}^{\prime} - h_{1}^{\prime} + {360{^\circ}}} & {{{{h_{1}^{\prime} - h_{2}^{\prime}}} > {180{^\circ}}},{h_{2}^{\prime} \leq h_{1}^{\prime}}} \\{h_{2}^{\prime} - h_{1}^{\prime} - {360{^\circ}}} & {{{{h_{1}^{\prime} - h_{2}^{\prime}}} \leq {180{^\circ}}},{h_{2}^{\prime} > h_{1}^{\prime}}}\end{matrix} \right.} & \left\lbrack {{Equation}\mspace{14mu} 11} \right\rbrack \\{\mspace{79mu}{{{\Delta\; H^{\prime}} = {\sqrt[2]{C_{1}^{\prime}C_{2}^{\prime}}{\sin\left( {\Delta\;{h^{\prime}/2}} \right)}}},{{\overset{\_}{H}}^{\prime} = \left\{ \begin{matrix}{\left( {h_{1}^{\prime} - h_{2}^{\prime}} \right)/2} & {{{h_{1}^{\prime} - h_{2}^{\prime}}} \leq {180{^\circ}}} \\{\left( {h_{1}^{\prime} - h_{2}^{\prime} + {360{^\circ}}} \right)/2} & {{{{h_{1}^{\prime} - h_{2}^{\prime}}} > {180{^\circ}}},{{h_{1}^{\prime} + h_{2}^{\prime}} < {360{^\circ}}}} \\{\left( {h_{1}^{\prime} - h_{2}^{\prime} + {360{^\circ}}} \right)/2} & {{{{h_{1}^{\prime} - h_{2}^{\prime}}} > {180{^\circ}}},{{h_{1}^{\prime} + h_{2}^{\prime}} \geq {360{^\circ}}}}\end{matrix} \right.}}} & \left\lbrack {{Equation}\mspace{14mu} 12} \right\rbrack \\{{T = {1 - {0.17\;\cos\;\left( {{\overset{\_}{H}}^{\prime} - {30{^\circ}}} \right)} + {0.24\;\cos\;\left( {2\;{\overset{\_}{H}}^{\prime}} \right)} + {0.32\;\cos\;\left( {{3{\overset{\_}{H}}^{\prime}} + {6{^\circ}}} \right)} - {0.20\;{\cos\left( {{4{\overset{\_}{H}}^{\prime}} + {63{^\circ}}} \right)}}}}\mspace{79mu}{S_{L} = {{1 + {\frac{0.015\left( {{\overset{\_}{L}}^{\prime} - 50} \right)^{2}}{\sqrt{20 + \left( {{\overset{\_}{L}}^{\prime} - 50} \right)^{2}}}\mspace{14mu} S_{C}}} = {1 + {0.045{\overset{\_}{C}}^{\prime}}}}}\mspace{14mu}\mspace{79mu}{S_{H} = {1 + {0.015{\overset{\_}{C}}^{\prime}T}}}{R_{T} = {{- 2}\sqrt{\frac{{\overset{\_}{C}}^{7}}{{\overset{\_}{C}}^{7} + 25^{7}}}{\sin\left\lbrack {60{{^\circ} \cdot {\exp\left( {- \left\lbrack \frac{{\overset{\_}{H}}^{\prime} - {275{^\circ}}}{25{^\circ}} \right\rbrack^{2}} \right)}}} \right\rbrack}}}} & \left\lbrack {{Equation}\mspace{14mu} 13} \right\rbrack\end{matrix}$

In Equations 4 to 13, the hue rotation period (R_(T)) deals with theissue with the blue area at a color angle around 275°, and the neutralcolor correction addresses the perceptual uniformity issue whileundergoing L*C*h-difference primed value, brightness compensation(S_(L)), color difference compensation (S_(C)), and tonal correction(S_(H)).

In this case, the color similarity analyzer 223 a enlarges or reducesthe size using the sliding window according to the preset increment ordecrement. The color similarity analyzer 223 a may measure thecamouflage performance of the camouflage pattern according to movementand perspective through convolution of the converted camouflage patternimage with the operation environment image, by moving the camouflagepattern.

The pattern similarity analyzer 223 b measures the degree of reflectionof the camouflage pattern image relative to the operation environmentimage using any one index of the structural similarity index measure(SSIM) and multiple structural similarity index measure (MSSIM) formeasuring the structural similarity index as shown in Equation 14 below.

$\begin{matrix}{{S\; S\; I\;{M\left( {x,y} \right)}} = \frac{\left( {{2\;\mu_{x}\mu_{y}} + c_{1}} \right)\left( {{2\;\sigma_{xy}} + c_{2}} \right)}{\left( {\mu_{x}^{2} + \mu_{y}^{2} + c_{1}} \right)\left( {\sigma_{x}^{2} + \sigma_{y}^{2} + c_{2}} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 14} \right\rbrack\end{matrix}$

In Equation 14, μ_(y) denotes the average color value of the operationenvironment image, μ_(y) denotes the average color value of thecamouflage pattern image, σ_(x) denotes the standard deviation of theoperation environment image, σ_(y) denotes the standard deviation of thecamouflage pattern image, σ_(xy) denotes the camouflage image colorcovariance, and c₁ and c₂ denote regularization thresholds.

MSSIM is a representative index used to compare image qualities, and ismainly used to measure the quality of color distribution reconstructionof an image, which is compressed (encoded) and then decompressed(decoded), as compared to the original image. The algorithm using MSSIMis an algorithm enhanced to be robust to image size changes, and MSSIMis an index proposed to overcome the disadvantages of peaksignal-to-noise ratio (PSNR) and to measure the degree of structuralreconstruction of the image. Therefore, the algorithm using MSSIM may beused as a measure to measure the degree of reflection of colordistribution of the camouflage pattern image as compared to theoperation environment image. In other words, the algorithm using MSSIMmeasures the distribution similarity in brightness (luminance),contrast, and structure between the original image and the reconstructedimage. If the distribution of elements for the brightness (luminance),contrast, and structure of the camouflage pattern image are similar tothe distribution of the operation environment image, the similarity hasa high value.

The deep learning-based object recognition algorithm used in thestructural similarity analysis step may be a convolutional neuralnetwork (CNN) including a visual geometry group network (VGGNET) and aresidual (ResNET). As illustrated in FIG. 7, the deep learning-basedobject recognition algorithm uses a VGG-19 network, which exhibitsexcellent performance in object recognition, to build a previouslytrained deep learning neural network. Here, VGG-19 is a convolutionalneural network (CNN) constituted of 19 layers.

The deep learning-based object recognition algorithm may input theoperation environment image and the camouflage pattern image to theVGG-19 network and extracts the respective feature maps of the operationenvironment image and camouflage pattern image from the second layer,i.e., conv2, of the VGG-19 network. Thereafter, the deep learning-basedobject recognition algorithm compares the extracted feature maps usingthe Frobenius norm and measures the structural similarity between theoperation environment image and the camouflage pattern image as shown inEquation 15.

$\begin{matrix}{{{structural}\mspace{14mu}{similarity}} = {\frac{1}{2}{\sum\limits_{i,j}\left( {E_{ij} - C_{ij}} \right)^{2}}}} & \left\lbrack {{Equation}\mspace{14mu} 15} \right\rbrack\end{matrix}$

In Equation 15, E_(ij) denotes the value of position j on the operationenvironment feature map in the filter of conv2_i, and C_(ij) denotes thevalue of position j on the camouflage feature map in the filter ofconv2_i.

As illustrated in FIG. 6, the deep learning-based object recognitionalgorithm may be a CNN including VGGNET and ResNET. A CNN generally hasthree types of layers.

The convolution layers divide the input image into several smallerimages using a 3×3 or larger-size convolution filter and activationfunction and extract a feature map. The subsampling layers extract afeature map with an average or maximum value of values through the x×xfilter to make the convolution feature map simpler and characteristic.The fully connected layers input the extracted feature values to theartificial neural network and upload them to a multidimensional vectorspace and classify them using matrix operation.

The deep learning-based object recognition algorithm classifies objectsaccording to categories based on deep learning, then learns them, andautomatically classifies which category of instance the newly givenimage is. Therefore, the deep learning-based object recognitionalgorithm gathers image data for the operation environment image andperforms preprocessing to assign a corresponding label to each image.Further, the deep learning-based object recognition algorithm extractsthe pixel-unit features of the image using a CNN model and then performsclassification training based on the values and then allows theso-trained model to predict the category for a new history image. Thedeep learning-based object recognition algorithm may performclassification training, targeting tree types/colors, leaf types/colors,or ground types/colors that appear frequently in the operationenvironment.

The artificial intelligence-based camouflage performance evaluationalgorithm evaluates the performance of the camouflage pattern inconjunction with the algorithm using MSSIM and deep learning-basedobject recognition algorithm which are algorithms for measuring thecolor similarity, pattern similarity, or structural similarity. Theabove-described algorithms are merely an embodiment for describing thedisclosure and, without being limited thereto, various changes ormodifications may be made thereto. Further, the above-describedalgorithms are stored in the memory 240 as a computer-readable recordingmedium that may be controlled by the processor 230. At least part of thealgorithm may be implemented in software, firmware, hardware, or acombination of at least two or more thereof and may include a module,program, routine, command set, or process for performing one or morefunctions.

The above-described embodiments may be implemented in the form ofrecording media including computer-executable instructions, such asprogram modules. The computer-readable medium may be an available mediumthat is accessible by a computer. The computer-readable storage mediummay include a volatile medium, a non-volatile medium, a separablemedium, and/or an inseparable medium. The computer-readable storagemedium may include a computer storage medium. The computer storagemedium may include a volatile medium, a non-volatile medium, a separablemedium, and/or an inseparable medium that is implemented in any methodor scheme to store computer-readable commands, data architecture,program modules, or other data or information.

Although embodiments of the disclosure have been described withreference to the accompanying drawings, it will be appreciated by one ofordinary skill in the art that the disclosure may be implemented inother various specific forms without changing the essence or technicalspirit of the disclosure. Thus, it should be noted that theabove-described embodiments are provided as examples and should not beinterpreted as limiting. Each of the components may be separated intotwo or more units or modules to perform its function(s) or operation(s),and two or more of the components may be integrated into a single unitor module to perform their functions or operations.

It should be noted that the scope of the disclosure is defined by theappended claims rather than the described description of the embodimentsand include all modifications or changes made to the claims orequivalents of the claims.

What is claimed is:
 1. A system for evaluating a camouflage patternbased on image analysis, the system comprising: a communication moduleproviding a communication interface interworking with a communicationmodule to provide a transmission/reception signal in a packet data form;a camouflage pattern evaluation module performing an artificialintelligence-based camouflage pattern evaluation algorithm on anoperation environment image of an operation environment and a camouflagepattern image of a camouflage pattern received through the communicationmodule, analyzing a similarity between the operation environment imageand the camouflage pattern image, and obtaining an evaluation result ofcamouflage performance for the camouflage pattern in the operationenvironment; and a processor deriving and providing a quantitativecamouflage performance value for the evaluation result of the camouflageperformance obtained by the camouflage pattern evaluation module,wherein the artificial intelligence-based camouflage performanceevaluation algorithm extracts feature information for the operationenvironment image and the camouflage pattern image and analyzes thesimilarity in color, pattern, or structure between the operationenvironment image and the camouflage pattern image based on theextracted feature information.
 2. The system of claim 1, wherein thecamouflage pattern evaluation module includes: an image input unitreceiving the operation environment image and the camouflage patternimage; an image preprocessor preprocessing the operation environmentimage and the camouflage pattern image through correction andnormalization; a similarity analyzer analyzing similarities in color,pattern, and structure between the operation environment image and thecamouflage pattern image preprocessed by the image preprocessor; and aresult providing unit averaging the similarities in color, pattern, andstructure between the operation environment image and the camouflagepattern image, calculating a weighted sum, and providing the evaluationresult of the camouflage performance.
 3. The system of claim 2, whereinthe similarity analyzer includes: a color similarity analyzercalculating a color similarity by performing color comparison on eachpixel between the operation environment image and the camouflage patternimage; a pattern similarity analyzer calculating a pattern similarity byperforming comparison on a color distribution in each space between theoperation environment image and the camouflage pattern image; and astructural similarity analyzer extracting respective structural featurevectors of the operation environment image and the camouflage patternimage using a deep learning-based object recognition algorithm andcalculating a structural similarity between the extracted structuralfeature vectors.
 4. The system of claim 1, wherein the camouflagepattern evaluation module scans the entire operation environment imageusing a sliding window scheme and calculates the similarity between theoperation environment image and the camouflage pattern image for eachsliding window.
 5. The system of claim 4, wherein the camouflage patternevaluation module repeats the calculation of the similarity between theoperation environment image and the camouflage pattern image for eachsliding window while resizing the sliding window by a preset incrementor decrement.
 6. A method for evaluating a camouflage pattern based onimage analysis performed by an image analysis system, the methodcomprising: receiving an operation environment image of an operationenvironment and a camouflage pattern image of a camouflage pattern;performing preprocessing on the operation environment image and thecamouflage pattern image through correction and normalization; analyzinga similarity between the operation environment image and the camouflagepattern image using an artificial intelligence-based camouflageperformance evaluation algorithm; and deriving and providing aquantitative camouflage performance value for an evaluation result of acamouflage performance for the camouflage pattern in the operationenvironment, wherein the artificial intelligence-based camouflageperformance evaluation algorithm extracts feature information for theoperation environment image and the camouflage pattern image andanalyzes the similarity in color, pattern, or structure between theoperation environment image and the camouflage pattern image based onthe extracted feature information.
 7. The method of claim 6, whereinperforming the preprocessing includes: converting the operationenvironment image and the camouflage pattern image, represented asred-green-blue (RGB) color space data, into XYZ color space data; andconverting the XYZ color space data into Lab color space data.
 8. Themethod of claim 6, wherein analyzing the similarity includes: a colorsimilarity analysis step calculating a color similarity by performingcolor comparison on each pixel between the operation environment imageand the camouflage pattern image; a pattern similarity analysis stepcalculating a pattern similarity by performing comparison on a colordistribution in each space between the operation environment image andthe camouflage pattern image; and a structural similarity analysis stepextracting respective structural feature vectors of the operationenvironment image and the camouflage pattern image using a deeplearning-based object recognition algorithm and calculating a structuralsimilarity between the extracted structural feature vectors.
 9. Themethod of claim 8, wherein the color similarity analysis step includesmeasuring the color similarity by converting the operation environmentimage and the camouflage pattern image, represented as RGB color spacedata, into Lab color space data and then calculating a color differencebased on a preset color difference equation between pixels matchingbetween the operation environment image and the camouflage pattern imagein the Lab color space data.
 10. The method of claim 8, wherein thepattern similarity analysis step includes measuring a degree ofreflection of a color distribution of the camouflage pattern image ascompared with the operation environment image using any one index of astructural similarity index measure (SSIM) or a multiple SSIM (MSSIM)for measuring a structural similarity index.
 11. The method of claim 8,wherein the deep learning-based object recognition algorithm used in thestructural similarity analysis step uses a convolutional neural network(CNN) including a visual geometry group network (VGGNET) and a residualnetwork (ResNET), and wherein the deep learning-based object recognitionalgorithm extracts respective feature maps of the operation environmentimage and the camouflage pattern image and compares the extractedfeature maps through a Frobenius norm to measure the structuralsimilarity between the operation environment image and the camouflagepattern image.
 12. The method of claim 6, wherein deriving and providingthe quantitative camouflage performance value includes deriving andproviding the quantitative camouflage performance value by multiplyingeach of similarities in color, pattern, or structure of the evaluationresult of the camouflage performance by a preset weight and summatingthe weight-multiplied similarities.